Pose Registration Model Improvement: Crease Detection. Diego Viejo Miguel Cazorla

Transcription

1 Pose Registration Model Improvement: Crease Detection Diego Viejo Miguel Cazorla Dpto. Ciencia de la Computación Dpto. Ciencia de la Computación e Inteligencia Artificial e Inteligencia Artificial Universidad de Alicante Universidad de Alicante Alicante Alicante dviejo@dccia.ua.es miguel@dccia.ua.es Resumen Several works deal with 3D data in SLAM problem. Data come from a 3D laser sweeping unit or a stereo camera, both providing a huge amount of data. In this paper, we detail an efficient method to find out creases from 3D raw data. This information can be used together with planar patches extracted from 3D raw data in order to build a complete 3D model of the scene. Some promising results are shown for both outdoor and indoor environments. 1. Introduction One of the central research themes in mobile robotics is the determination of the movement performed by the robot using its sensors information. This methods are called pose registration and can be used for automatic map building and SLAM [14] [9] [10]. The problem can be seen as finding out the correspondences between two consecutive sensor readings and then computing the transformation, which is the robot movement, from the correspondences. In real 3 dimensional scenes the problem increases because the huge amount of information [17]. Modeling methods can be used to reduce scene complexity [15] [16] [20]. Our main goal is to perform pose registration in semi-structured environments, i.e., man-made indoor and outdoor environments. We use a sweeping unit with a 2D laser Sick or a Digiclops stereo camera as a main sensor, mounted on a mobile robot. Sweeping laser provides 3D data with low error and higher range compared to stereo systems [11]. However, our main aim is to deal with outliers, i.e., environments with people or not modeled objects. This task is hard to manage because classics algorithms, like ICP [18] and its variants [19], are very sensitive to outliers. Furthermore, we will not use odometry information. In [12] we propose a method for pose registration using 3D range data. We obtain a planar patches model from the raw data and perform over the reduced model a modified ICP-based method to achieve registration between two consecutive poses. This approach is reasonably successful when a high scope sensor is used. Nevertheless, for scenes captured with a stereo system, an improved model is needed. In the present work we propose to improve this model representation by adding crease information to the model. Surface creases have numerous applications in geometric modeling [3][4][5], image processing [6][7], other fields [8], and also for the resolving the SLAM problem [13]. To find out creases on a surface, two actions have to be performed. First, a local estimation of the curvature is computed along the hole surface. Second, an analysis of this curvature is carried out to find the maxima and minima points on the surface. Also, a previous smoothing process may improve results [1][2]. In this paper we propose a new method for extracting creases by using the algorithm described in [11] to also label points in a 3D scene as posible creases. Then a posterior region growing algorithm

2 is used to extract complete crease descriptions. The rest of the paper is organized as follows. In Section 2 we present the data acquisition process and describe the system used. Then, Section 3 details how we can improve the reduced model representation, extracting scene creases. Several experiments are described in Section 4. We conclude in Section 5 with our future work. its environment. As it moves it gets data from the sweeping unit. The robot takes N 3D observations performed from different poses. Assuming that the robot is confined to the XZ horizonal plane and also that the data capture system is fixed, each pose can be described as coordinates in the horizontal plane and a rotation around the vertical axis Y. Given a pose, the next one is obtained through the incremental action at = [δxt, δzt, δθt ] between two consecutive observations (3D acquisitions). Figure 2 shows an example of data obtained in both indoor and outdoor environment. Each data set is a 3D point cloud. The entire 3D scanner is mounted on a PowerBot from Mobile Robotics (see Figure 1 Up). This kind of robot allow us to use it in outdoor scenarios. On the other hand, data come from a Digiclops stereo camera. This 3D range sensor grab reliable data up to 5 meters as we pointed out in [11]. The camera is mounted on a ER1 (Figure 1 Down) from Evolution Robotics. This robot is mainly designed to act into indoor environments. For robot movements, the same assumptions than in the sweeping unit case are considered. 3. Figura 1: Robots and 3D scanners used in our experiments. Up: Our PowerBot equipped with laser Sick and a sweeping unit. Bottom: the ER-1 with a Digiclops stereo camera attached on it. 2. Data Acquisition We are looking for a general method to improve 3D models. For this reason we grab data from different 3D range sensors. On the one hand, data come from a standard 2D Sick laser with a laser sweeping unit. This unit allows to capture 3D data. The robot is driven around Model Improvement We can reduce a raw 3D scene complexity by searching for the planar surfaces into the scene. Time and memory consumptions are improved with this complexity reduction. Nevertheless, since our main goal consists of resolving SLAM problem, the approach of representing a 3D scene only by mean of planar surfaces may be not enough in some situations. For example, when the range sensor used to acquire data can not retrieve anything far enough, this model representation doesn t work at all. In these situations, we need extra information about environment in order to fit robot movements such as crease surface information. As we pointed out before, we are going to use the method that we applied in planar surface estimation in order to find out surface creases too. In few words, this method consist of computing normal vector of each point in a

3 Figura 2: 3D data from the sweeping unit. Left: indoor environment. Floor and roof points have been removed to improve visualisation. Right: outdoor. Figura 3: Singular Value Decomposition results. Left: when points belongs to a plane the singular vector related with the minimum singular value is the normal vector of the surface. Middle: when points arise from a crease the singular vector related with the maximum singular value is the director vector of the crease. Right: when points are uniformly distributed all singular values are equivalent 3D scene by mean of a singular value decomposition over the spatial covariance matrix in the neighbourhood of points. Singular values also gives us information about the surface in this neighbourhood as we can see in Figure 3. When the underlying surface is a plane, the minimum singular value is quite smaller than the other two singular values, and the singular vector related with this minimum singular value is the normal vector of the surface at this point. On the other hand, creases usually comes from building corners, window and door frames, trunk trees, and so on. In these situations, the maximum singular value is quite higher than the others and the singular vector related with the maximum singular value is tangent to the surface and its direction fits the direction of the crease. From this information we can label each point in a 3D scene as belonging to a planar surface, when one of the singular values is much smaller than the others; belonging to a crease, when one of the singular values is much higher than the others; or not defined objects in other case. In Figure 4 we can see an example of applying this segmentation for both outdoor and indoor scenes. Despite the segmentation of the scene points, we need to do some extra work to fit creases in the scene. Since there is noise in the scene because of measurement errors, some points labelled as creases in fact are not or its director vector doesn t fit with the direction of the real crease. As we can see in Figure 5, all director vectors of points that really fit to creases are ordered. We can exploit this feature to cluster points with similar director vectors that belong to the same crease.

4 Figura 5: Crease directions computed at each crease point. Director vector of the points fits the direction of real creases and are aligned with its neighbour director vector when they have been well labelled as creases. If points have been bad labelled, no alignment between director vectors can be noticed. Figura 4: Labelled of points. Yellow points represent planar surfaces, blue points represent creases and green points represent non-defined objects into the scene. Up image shows the results of labelling an outdoor scene from a laser range scanner; Middle and bottom image labelling come from points in indoor scenes grabbed with a laser range scanner and a stereo vision system respectively. We use a merging algorithm in order to obtain the creases of a scene. A crease C i( p i, d i, l i, w i) is described by four parameters: its position vector, its director vector, its length and its width. The width measures the mean of the distance from the crease to its supporting points. First of all, we consider all points previously labelled as crease to be small creases through the whole scene. A pair of creases C i and C j will be merged if they fulfil crease constraint, it is said, if the angle between d i and d j vectors is under a threshold α and p i lies on the crease C j with an error margin under l j + β along the direction of the crease and under w j + γ in the perpendicular direction of the crease. α, β and γ are empirically started to 0.2 rad, 0.15 m and 0.02 m respectively. The merging algorithm has two steps that are iterated until no more merges can be found. In the first step, all posible merges are performed using the previous criterium. The points that support each new crease are retained. The second step consist of computing creases parameters from its supporting points. When this process finishes, creases with few

5 supporting points are removed. 4. Results Data were taken using both the 3D range laser scanner and the stereo camera described in Section 2. We perform our experiments in both indoors and outdoors at the campus of the University of Alicante. Outdoor scenes are formed by a semi-structured scenario formed by alone buildings surrounded by open green areas and trees. Figure 2 (right) shows a campus outdoor point set. It can be noticed that non structured objects such as trees, streetlamps or shrubs produce partial occlusions of the planar surfaces of the environment. 3D images were taken at irregular intervals from 0.5 up to 2.0 meters and 0 to π/4 radians. During the experiments, people were walking freely around the robot, which introduce noise into data. Figure 6 shows a free view of an outdoor 3D scene obtained with a laser 3D. Creases are represented by mean of green coloured cylinders. The length and the radius of each cylinder come from the length and width of each crease respectively. It can be noticed that building squares, trunk trees and streetlights are detected as creases. Figure 7 shows the results of another 3D scene obtained by mean of a sweeping SICK laser, but in an indoor environment. This is also a free view of the scene. In this experiment, the environment was hardly noisy since lots of students were moving through the building. In spite of this dynamic environment we successfully achieve to recover most of the walls, floor and ceiling intersections. Figura 7: Creases extracted from an indoor 3D scene. Data come from a sweeping SICK unit mounted on a PowerBot. Wall s boundaries are marked as creases. In Figure 8 we can observe the results of applying our approach to perform crease detection over an scene obtained from a Digiclops stereo camera. In this experiment we have to deal with the high level of noise of the data. This may cause some problems with crease detection. As we can see, walls and floor intersections are well extracted. Nevertheless, we fail extracting creases in the limits of the data set. 5. Conclusions and Future Work Figura 6: Creases extracted from an outdoor 3D scene. Data come from a sweeping SICK unit mounted on a PowerBot. Building squares, trunk trees and streetlights are marked as creases. In this paper we have presented a method to find out creases in a 3D scene. This method uses the computations used to carry out planar patches extraction so it is a light process. Planar patches and creases can be used together to obtain the movement performed by a mobile instead of just using planar patches. First, we extract 3D planar patches from dense maps obtained from a 3D laser sweeping unit.

6 [2] Alexander G. Belyaev, Elena V. Anoshkina: Detection of Surface Creases in Range Data. IMA Conference on the Mathematics of Surfaces 2005: [3] Yu. Ohtake, A. Belyaev, and H.-P. Seidel. Ridge-valley lines on meshes via implicit surface fitting. ACM Transactions on Graphics, 23(3): , August Proceedings of ACM SIGGRAPH Figura 8: Creases extracted from an indoor 3D scene. Data come from a Digiclops stereo camera mounted on a ER1 robot. Wall s boundaries are marked as creases. The bottom crease corresponds to the boundary between floor and wall. The right one is a corner. The above creases are extracted due to the limits of the camera field of view. This process is efficient and allow us to reduce the size of the problem without loosing too much information. Several experiments show the validity of the method. As future work, we plan to measure the quantitative error in the method for crease extraction. Furthermore, we want to include this information in our planar patches based pose registration or as natural landmarks in a SLAM problem. Acknowledgment This work has been supported by project GV06/134 from Generalitat Valenciana (Spain) and project DPI from Ministerio de Educación y Ciencia (Spain). Referencias [1] PAGE, D. L., SUN, Y., KOSCHAN, A., PAIK, J., AND ABIDI, M Normal vector voting: Crease detection and curvature estimation on large, noisy meshes. Journal of Graphical Models 64, [4] X. Pennec, N. Ayache, and J. P. Thirion. Landmark-based registration using geatures identified through differential geometry. In I. N. Bankman, editor, Handbook of Medical Imgaging. Academic Press, [5] G. Stylianou and G. Farin. Crest lines form surface segmentation and flattening. IEEE Transactions on Visulization and Computer Graphics, 10(5): , [6] N. M. Patrikalakis and T. Maekawa. Shape Interrogation for Computer Aided Design and Manufacturing. Springer, Heidelberg, [7] P. Perona and J. Malik. Scale-space and edge detection using anisotropid diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7): , July [8] P. L. Hallinan, G. G. Gordon, A. L. Yuille, P Giblin, and D. Mumford. Two- and Three-Dimensional Patterns of the Face. A K Peters, [9] G. Dissanayake, P. Newman, S. Clark, H. Durrant-White, M. Csorba. A solution to the simultaneus localization and map building (SLAM) problem. IEEE Transactions on Robotics and Autonomation. 17 (3). pp [10] M. Montemerlo, S. Thrun, D. Koller, B. Webgreit. FastSLAM 2.0: An improved particle filtering algorithm for simultaneus localization and mappling that probably converges. In proceedings of the International Joint Conference on Artificial Intelligence, IJCAI, 2003.

7 [11] D. Viejo and M. Cazorla Plane Extraction and Error Modeling of 3D data 5th International Symposium on Robotics and Automation. Mexico. Agosto, [12] D. Viejo and M. Cazorla 3D Planebased Egomotion for SLAM on Semistructured Environment IEEE/RSJ International Conference on Intelligent Robots and Systems. In reviewing process [13] David M. Cole, Alastair R. Harrison, Paul M. Newman. Using Naturally Salient Regions for SLAM with 3D Laser Data. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2005 [14] David M. Cole and Paul M. Newman. Using Laser Range Data for 3D SLAM in Outdoor Environments. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2006 [15] R. Lakaemper and L.J. Latecki. Extended EM for Planer Approximation of 3D Data. Proceedings of the 2006 IEEE International Conference on Robotics and Automation. Orlando, Florida. May [16] R. Triebel, W. Burgard and F. Dellaert, Using Hierarchical EM to Extract Planes from 3D Range Scans. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2005 [17] Weingarten, J. and Siegwart, R. EKFbased 3D SLAM for Structured Environment Reconstruction. In Proceedings of IROS, Edmonton, Canada, August 2-6, (IROS 2005) [18] P. J. Besl and N. D. McKay. A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell., 14(2): , 1992 [19] S. Rusinkiewicz and M. Levoy. Efficient Variants of the ICP Algorithm. Third International Conference on 3D Digital Imaging and Modeling (3DIM) [20] M. Martin Nevado, J. Gomez Garcia- Bermejo, E. Zalama Casanova. Obtaining 3D models of indoor environments with a mobile robot by estimating local surface directions. Robotics and Autonomous Systems. Elsevier.Vol 48/2-3. pp